TY - GEN
T1 - Augmented Sensory Feedback during Training of Upper Extremity Function in Virtual Reality
AU - Shi, Yu
AU - Liu, Mingxiao
AU - Dewil, Sophie
AU - Harel, Noam Y.
AU - Sanford, Sean
AU - Nataraj, Raviraj
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study examined how varying the type (visual, haptic, or visual plus haptic) of augmented sensory feedback (ASF) cues used for guidance during motor training with virtual reality (VR) can uniquely impact performance and physiological responses indicative of cognitive load and physical arousal. This work aims to demonstrate the need and opportunity to optimize VR approaches for motor rehabilitation based on how the ASF is provided. This study utilized a custom VR platform for rehabilitating upper-body function for a myoelectric control task. Neurotypical participants (n=15) exerted near-isometric muscular exertions, and the resultant electromyographic (EMG) recordings were input to a support vector machine for commanding movement of a VR robot arm tasked to contact various targets. Motor performance was based on minimizing the motion pathlength of the robot end-effector and minimizing the time to complete each trial. Skin-surface electroencephalography (EEG) and electrodermal activity (EDA) were measured to assess cognitive loading and physical arousal, respectively. For this task, multimodal ASF (i.e., visual plus haptic cues) generated the highest cognitive load as expected; however, visual ASF produced more arousal and most improved post-training performance. Our findings suggest how participant performance and physiological states are related and governable by ASF. Thus, computerized approaches, such as VR, for motor training may be optimized based on how sensory-driven guidance cues are delivered.
AB - This study examined how varying the type (visual, haptic, or visual plus haptic) of augmented sensory feedback (ASF) cues used for guidance during motor training with virtual reality (VR) can uniquely impact performance and physiological responses indicative of cognitive load and physical arousal. This work aims to demonstrate the need and opportunity to optimize VR approaches for motor rehabilitation based on how the ASF is provided. This study utilized a custom VR platform for rehabilitating upper-body function for a myoelectric control task. Neurotypical participants (n=15) exerted near-isometric muscular exertions, and the resultant electromyographic (EMG) recordings were input to a support vector machine for commanding movement of a VR robot arm tasked to contact various targets. Motor performance was based on minimizing the motion pathlength of the robot end-effector and minimizing the time to complete each trial. Skin-surface electroencephalography (EEG) and electrodermal activity (EDA) were measured to assess cognitive loading and physical arousal, respectively. For this task, multimodal ASF (i.e., visual plus haptic cues) generated the highest cognitive load as expected; however, visual ASF produced more arousal and most improved post-training performance. Our findings suggest how participant performance and physiological states are related and governable by ASF. Thus, computerized approaches, such as VR, for motor training may be optimized based on how sensory-driven guidance cues are delivered.
KW - augmented sensory feedback
KW - motor rehabilitation
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85200475249&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85200475249&partnerID=8YFLogxK
U2 - 10.1109/CBMS61543.2024.00046
DO - 10.1109/CBMS61543.2024.00046
M3 - Conference contribution
AN - SCOPUS:85200475249
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 231
EP - 236
BT - Proceedings - 2024 IEEE 37th International Symposium on Computer-Based Medical Systems, CBMS 2024
A2 - Ochoa-Ruiz, Gilberto
A2 - Grisan, Enrico
A2 - Ali, Sharib
A2 - Sicilia, Rosa
A2 - Santamaria, Lucia Prieto
A2 - Kane, Bridget
A2 - Daul, Christian
A2 - Ante, Gildardo Sanchez
A2 - Gonzalez, Alejandro Rodriguez
T2 - 37th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2024
Y2 - 26 June 2024 through 28 June 2024
ER -